Cooper details many of these meta functions to explain his central thesis: programmers need to seriously re-evaluate the many user-hostile concepts deeply embedded within the software development process. Rather than provide users with a straightforward set of options, programmers often pile on the bells and whistles and ignore or de-prioritise lingering bugs. For the average user, increased functionality is a great burden, adding to the recurrent chorus that plays: "computers are hard, mysterious, unwieldy things." (An average user, Cooper asserts, who doesn't think that way or who has memorised all the esoteric commands and now lords it over others, has simply been desensitised by too many years of badly designed software.) Cooper's writing style is often overblown, with a pantheon of cutesy terminology (i.e. "dancing bearware") and insider back-patting. (When presenting software to Bill Gates, he reports that Gates replied: "How did you do that?" to which he writes: "I love stumping Bill!") More seriously, he is also unable to see beyond software development's importance--a sin he accuses programmers of throughout the book. Even with that in mind, the central questions Cooper asks are too important to ignore: Are we making users happier? Are we improving the process by which they get work done? Are we making their work hours more effective? Cooper looks to programmers, business managers and what he calls "interaction designers" to question current assumptions and mindsets. Plainly, he asserts that the goal of computer usage should be "not to make anyone feel stupid." Our distance from that goal reinforces the need to rethink entrenched priorities in software planning. -- Jennifer Buckendorff, Amazon.com

    It's great to have theory and evidenced-based research at your disposal, but what will you do when somebody asks you to make your dashboard 'cooler' by adding packed bubbles and donut charts?The expert authors have a combined 30-plus years of hands-on experience helping people in hundreds of organizations build effective visualizations. They have fought many 'best practices' battles and having endured bring an uncommon empathy to help you, the reader of this book, survive and thrive in the data visualization world.A well-designed dashboard can point out risks, opportunities, and more; but common challenges and misconceptions can make your dashboard useless at best, and misleading at worst. The Big Book of Dashboards gives you the tools, guidance, and models you need to produce great dashboards that inform, enlighten, and engage.

    We need a brand new way to take it all in. 'Information is Beautiful' transforms the ideas surrounding and swamping us into graphs and maps that anyone can follow at a single glance.

    Cracking the PM Interview is a comprehensive book about landing a product management role in a startup or bigger tech company. Learn how the ambiguously-named "PM" (product manager / program manager) role varies across companies, what experience you need, how to make your existing experience translate, what a great PM resume and cover letter look like, and finally, how to master the interview: estimation questions, behavioral questions, case questions, product questions, technical questions, and the super important "pitch."

    He guides you, step by step, as you discover how to combine the tenets of classic storytelling with the power of the projected media to create a rich, engaging experience. He walks you through his easy-to-use templates, plus 50 advanced tips, to help build your confidence and effectiveness—and quickly bring your ideas to life!FOCUS: Learn how to distill your best ideas into a crisp and compelling narrative.CLARIFY: Use a storyboard to clarify and visualize your ideas, creating the right blend of message and media.ENGAGE:Move from merely reading your slides to creating a rich, connected experience with your audience—and increase your impact!Inside!: See sample storyboards for a variety of presentation types—including investment, sales, educational, and training.

    How can we catch schools that cheat on standardized tests? How does Netflix know which movies you’ll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more.For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal—and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.

    H. Evergreen, Effective Data Visualization shows readers how to create Excel charts and graphs that best communicate data findings. This comprehensive how-to guide functions as a set of blueprints--supported by research and the author's extensive experience with clients in industries all over the world--for conveying data in an impactful way. Delivered in Evergreen's humorous and approachable style, the book covers the spectrum of graph types available beyond the default options, how to determine which one most appropriately fits specific data stories, and easy steps for making the chosen graph in Excel.

    Three dots to represent Dallas, Houston, and San Antonio. Three arrows to show direct flights. Problem solved, and the picture made it easy to sell Southwest Airlines to investors and customers. Used properly, a simple drawing on a humble napkin is more powerful than Excel or PowerPoint. It can help crystallize ideas, think outside the box, and communicate in a way that people simply “get”. In this book Dan Roam argues that everyone is born with a talent for visual thinking, even those who swear they can’t draw. Drawing on twenty years of visual problem solving combined with the recent discoveries of vision science, this book shows anyone how to clarify a problem or sell an idea by visually breaking it down using a simple set of visual thinking tools – tools that take advantage of everyone’s innate ability to look, see, imagine, and show. THE BACK OF THE NAPKIN proves that thinking with pictures can help anyone discover and develop new ideas, solve problems in unexpected ways, and dramatically improve their ability to share their insights. This book will help readers literally see the world in a new way.

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details

    Over one hundred stories drawn from the front lines of business and government, as well as myths, fables, and parables from around the world, illustrate how story can be used to persuade, motivate, and inspire in ways that cold facts, bullet points, and directives can’t. These stories, combined with practical storytelling techniques show anyone how to become a more effective communicator. From “who I am” to “I-know-what-you’re thinking,” Simmons identifies the six stories you need to know how to tell and demonstrates how they can be applied. This revised edition offers a guide to using storytelling in specific business circumstances, including corporate reorganizations, layoffs, and diversity issues.

    Most of us approach this task by converting a written document into slides, but the result is often a text-heavy presentation saddled with bullet points, stock images, and graphs too complex for an audience to decipher--much less understand. Presenting is fundamentally different from writing, and with only a little more time, a little more effort, and a little more planning, you can communicate your work with force and clarity.Designed for presenters of scholarly or data-intensive content, "Better Presentations "details essential strategies for developing clear, sophisticated, and visually captivating presentations. Following three core principles--visualize, unify, and focus--"Better Presentations" describes how to visualize data effectively, find and use images appropriately, choose sensible fonts and colors, edit text for powerful delivery, and restructure a written argument for maximum engagement and persuasion. With a range of clear examples for what to do (and what not to do), the practical package offered in" Better Presentations" shares the best techniques to display work and the best tactics for winning over audiences. It pushes presenters past the frustration and intimidation of the process to more effective, memorable, and persuasive presentations.

    Douglas Hubbard helps us create a path to know the answer to almost any question in business, in science, or in life . . . Hubbard helps us by showing us that when we seek metrics to solve problems, we are really trying to know something better than we know it now. How to Measure Anything provides just the tools most of us need to measure anything better, to gain that insight, to make progress, and to succeed." -Peter Tippett, PhD, M.D. Chief Technology Officer at CyberTrust and inventor of the first antivirus software "Doug Hubbard has provided an easy-to-read, demystifying explanation of how managers can inform themselves to make less risky, more profitable business decisions. We encourage our clients to try his powerful, practical techniques." -Peter Schay EVP and COO of The Advisory Council "As a reader you soon realize that actually everything can be measured while learning how to measure only what matters. This book cuts through conventional cliches and business rhetoric and offers practical steps to using measurements as a tool for better decision making. Hubbard bridges the gaps to make college statistics relevant and valuable for business decisions." -Ray Gilbert EVP Lucent "This book is remarkable in its range of measurement applications and its clarity of style. A must-read for every professional who has ever exclaimed, 'Sure, that concept is important, but can we measure it?'" -Dr. Jack Stenner Cofounder and CEO of MetraMetrics, Inc.

    This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases